BackgroundReduced representation bisulfite sequencing (RRBS) has been widely used to profile genome-scale DNA methylation in mammalian genomes. However, the applications and technical performances of RRBS with different fragment sizes have not been systematically reported in pigs, which serve as one of the important biomedical models for humans. The aims of this study were to evaluate capacities of RRBS libraries with different fragment sizes to characterize the porcine genome.ResultsWe found that the MspI-digested segments between 40 and 220 bp harbored a high distribution peak at 74 bp, which were highly overlapped with the repetitive elements and might reduce the unique mapping alignment. The RRBS library of 110–220 bp fragment size had the highest unique mapping alignment and the lowest multiple alignment. The cost-effectiveness of the 40–110 bp, 110–220 bp and 40–220 bp fragment sizes might decrease when the dataset size was more than 70, 50 and 110 million reads for these three fragment sizes, respectively. Given a 50-million dataset size, the average sequencing depth of the detected CpG sites in the 110–220 bp fragment size appeared to be deeper than in the 40–110 bp and 40–220 bp fragment sizes, and these detected CpG sties differently located in gene- and CpG island-related regions.ConclusionsIn this study, our results demonstrated that selections of fragment sizes could affect the numbers and sequencing depth of detected CpG sites as well as the cost-efficiency. No single solution of RRBS is optimal in all circumstances for investigating genome-scale DNA methylation. This work provides the useful knowledge on designing and executing RRBS for investigating the genome-wide DNA methylation in tissues from pigs.

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The objectives of this study were to estimate the genetic parameters and the breeding progress in a Landrace herd in China, and to predict the potential benefits by applying new breeding technology. Hereby, the performance records from a Landrace swine herd in China, composing over 33000 pigs born between 2001 and 2013, were collected on six economically important traits, average daily gain between 30-100 kg (ADG), adjusted backfat thickness at 100 kg (BF), adjusted days to 30 kg (D30), adjusted days to 100 kg (D100), number born alive (NBA), and total number born (TNB). The genetic parameters were estimated by restricted maximum likelihood via DMU, and realized genetic trends were analyzed. Based on the real population structure and genetic parameters obtained from this herd, the potential genetic trends by applying genomic selection (GS) were predicted via a computer simulation study. Results showed that the heritability estimates in this Landrace herd were 0.55 (0.02), 0.42 (0.01), and 0.12 (0.01), for BF, D100, and TNB, respectively. Favorable genetic trends were obtained for D100, BF, and TNB due to direct selection, for ADG and NBA due to indirect selection. Long-term selection against D100 did not improve D30, though they are highly genetically correlated (0.64). Appling GS in such a swine herd, the genetic gain can be increased by 25%, or even larger for traits with low heritability or individuals without phenotypes before selection. It can be concluded that conventional breeding strategy was effective in the herd studied, while applying GS is promising and hence the road ahead in swine breeding.